Papers with Explanations
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020.acl-demos)
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Dong-Ho Lee, Rahul Khanna, Bill Yuchen Lin, Seyeon Lee, Qinyuan Ye, Elizabeth Boschee, Leonardo Neves, Xiang Ren
| Challenge: | Existing frameworks for sequence labeling and classification require massive human effort and labeling data is limited. |
| Approach: | They propose a web-based, Label-Efficient AnnotatioN framework that allows an annotator to provide the needed labels for a task and can capture explanations for each labeling decision. |
| Outcome: | The proposed framework surpasses baseline F1 scores by 5-10 percentage points while using 2X times fewer labeled instances. |
Enhancing Multi-party Dialogue Discourse Parsing with Explanation Generation (2025.coling-main)
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| Challenge: | Multi-party dialogue discourse parsing is an important and challenging task in natural language processing. |
| Approach: | They propose a model to integrate external knowledge from Large Language Models to analyze dialogue discourse structures and semantic relations between utterances in multi-party conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) models on two public datasets. |
What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments (D19-1)
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| Challenge: | Explanations are central to everyday life, and are a topic of growing interest in the AI community. |
| Approach: | They propose a word-level prediction task to investigate how explanations selectively reuse information from what is being explained. |
| Outcome: | The proposed features have strong predictive power on the echoing of a word in an explanation, and enhance neural methods of generating explanations. |
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (2022.acl-long)
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| Challenge: | Existing neural approaches to solve algebraic word problems have a plausible answer, but this belief has less been verified due to Q. |
| Approach: | They propose a neural model EPT-X which utilizes natural language explanations to solve an algebraic word problem. |
| Outcome: | The proposed model achieves an average performance of 69.59% on a PEN dataset and produces explanations with quality comparable to human output. |
Can LLMs Explain Themselves Counterfactually? (2025.emnlp-main)
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| Challenge: | Explanations are an important tool for gaining insights into model behavior, calibrating user trust, and ensuring compliance. |
| Approach: | They propose to use self-explanation to prompt models to explain outputs . they find that LLMs struggle to generate SCEs - their prediction often does not agree with their own counterfactual reasoning. |
| Outcome: | The proposed methods can generate SCEs across families, sizes, temperatures, and datasets. |
Modeling the Quality of Dialogical Explanations (2024.lrec-main)
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Milad Alshomary, Felix Lange, Meisam Booshehri, Meghdut Sengupta, Philipp Cimiano, Henning Wachsmuth
| Challenge: | Existing studies have focused on the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. |
| Approach: | They construct a corpus of 399 reddit dialogues and analyze interaction flows and explainee quality using two language models that can handle long inputs. |
| Outcome: | The proposed model predicts that the interaction flows between the explainer and the explainee correlate with the quality of the explanations in terms of a successful understanding on the explain's side. |